


'''simple docstring'''






import argparse
import os
import re

import tensorflow as tf
import torch

from transformers import BertConfig, BertModel
from transformers.utils import logging


logging.set_verbosity_info()
__UpperCAmelCase       							=				logging.get_logger(__name__)


def    _snake_case  (	A					,       A					,       A			)		->       Union[str, Any]:
							lowerCAmelCase__			  =      os.path.abspath(A			)
							logger.info(F"""Converting TensorFlow checkpoint from {tf_path}"""			)
							# Load weights from TF model
							lowerCAmelCase__			  =      tf.train.list_variables(A			)
							lowerCAmelCase__			  =      []
							lowerCAmelCase__			  =      []
							lowerCAmelCase__			  =      []
							for full_name, shape in init_vars:
														# logger.info(f"Loading TF weight {name} with shape {shape}")
														lowerCAmelCase__			  =      full_name.split('''/'''			)
														if full_name == "_CHECKPOINTABLE_OBJECT_GRAPH" or name[0] in ["global_step", "save_counter"]:
																					logger.info(F"""Skipping non-model layer {full_name}"""			)
																					continue
														if "optimizer" in full_name:
																					logger.info(F"""Skipping optimization layer {full_name}"""			)
																					continue
														if name[0] == "model":
																					# ignore initial 'model'
																					lowerCAmelCase__			  =      name[1:]
														# figure out how many levels deep the name is
														lowerCAmelCase__			  =      0
														for _name in name:
																					if _name.startswith('''layer_with_weights'''			):
																												depth += 1
																					else:
																												break
														layer_depth.append(A			)
														# read data
														lowerCAmelCase__			  =      tf.train.load_variable(A					,       A			)
														names.append('''/'''.join(A			)			)
														arrays.append(A			)
							logger.info(F"""Read a total of {len(A			):,} layers"""			)

							# Sanity check
							if len(set(A			)			) != 1:
														raise ValueError(F"""Found layer names with different depths (layer depth {list(set(A			)			)})"""			)
							lowerCAmelCase__			  =      list(set(A			)			)[0]
							if layer_depth != 1:
														raise ValueError(
														    '''The model contains more than just the embedding/encoder layers. This script does not handle MLM/NSP'''
														    ''' heads.'''			)

							# convert layers
							logger.info('''Converting weights...'''			)
							for full_name, array in zip(A					,       A			):
														lowerCAmelCase__			  =      full_name.split('''/'''			)
														lowerCAmelCase__			  =      model
														lowerCAmelCase__			  =      []
														for i, m_name in enumerate(A			):
																					if m_name == ".ATTRIBUTES":
																												# variable names end with .ATTRIBUTES/VARIABLE_VALUE
																												break
																					if m_name.startswith('''layer_with_weights'''			):
																												lowerCAmelCase__			  =      int(m_name.split('''-'''			)[-1]			)
																												if layer_num <= 2:
																																			# embedding layers
																																			# layer_num 0: word_embeddings
																																			# layer_num 1: position_embeddings
																																			# layer_num 2: token_type_embeddings
																																			continue
																												elif layer_num == 3:
																																			# embedding LayerNorm
																																			trace.extend(['''embeddings''', '''LayerNorm''']			)
																																			lowerCAmelCase__			  =      getattr(A					,       '''embeddings'''			)
																																			lowerCAmelCase__			  =      getattr(A					,       '''LayerNorm'''			)
																												elif layer_num > 3 and layer_num < config.num_hidden_layers + 4:
																																			# encoder layers
																																			trace.extend(['''encoder''', '''layer''', str(layer_num - 4			)]			)
																																			lowerCAmelCase__			  =      getattr(A					,       '''encoder'''			)
																																			lowerCAmelCase__			  =      getattr(A					,       '''layer'''			)
																																			lowerCAmelCase__			  =      pointer[layer_num - 4]
																												elif layer_num == config.num_hidden_layers + 4:
																																			# pooler layer
																																			trace.extend(['''pooler''', '''dense''']			)
																																			lowerCAmelCase__			  =      getattr(A					,       '''pooler'''			)
																																			lowerCAmelCase__			  =      getattr(A					,       '''dense'''			)
																					elif m_name == "embeddings":
																												trace.append('''embeddings'''			)
																												lowerCAmelCase__			  =      getattr(A					,       '''embeddings'''			)
																												if layer_num == 0:
																																			trace.append('''word_embeddings'''			)
																																			lowerCAmelCase__			  =      getattr(A					,       '''word_embeddings'''			)
																												elif layer_num == 1:
																																			trace.append('''position_embeddings'''			)
																																			lowerCAmelCase__			  =      getattr(A					,       '''position_embeddings'''			)
																												elif layer_num == 2:
																																			trace.append('''token_type_embeddings'''			)
																																			lowerCAmelCase__			  =      getattr(A					,       '''token_type_embeddings'''			)
																												else:
																																			raise ValueError(F"""Unknown embedding layer with name {full_name}"""			)
																												trace.append('''weight'''			)
																												lowerCAmelCase__			  =      getattr(A					,       '''weight'''			)
																					elif m_name == "_attention_layer":
																												# self-attention layer
																												trace.extend(['''attention''', '''self''']			)
																												lowerCAmelCase__			  =      getattr(A					,       '''attention'''			)
																												lowerCAmelCase__			  =      getattr(A					,       '''self'''			)
																					elif m_name == "_attention_layer_norm":
																												# output attention norm
																												trace.extend(['''attention''', '''output''', '''LayerNorm''']			)
																												lowerCAmelCase__			  =      getattr(A					,       '''attention'''			)
																												lowerCAmelCase__			  =      getattr(A					,       '''output'''			)
																												lowerCAmelCase__			  =      getattr(A					,       '''LayerNorm'''			)
																					elif m_name == "_attention_output_dense":
																												# output attention dense
																												trace.extend(['''attention''', '''output''', '''dense''']			)
																												lowerCAmelCase__			  =      getattr(A					,       '''attention'''			)
																												lowerCAmelCase__			  =      getattr(A					,       '''output'''			)
																												lowerCAmelCase__			  =      getattr(A					,       '''dense'''			)
																					elif m_name == "_output_dense":
																												# output dense
																												trace.extend(['''output''', '''dense''']			)
																												lowerCAmelCase__			  =      getattr(A					,       '''output'''			)
																												lowerCAmelCase__			  =      getattr(A					,       '''dense'''			)
																					elif m_name == "_output_layer_norm":
																												# output dense
																												trace.extend(['''output''', '''LayerNorm''']			)
																												lowerCAmelCase__			  =      getattr(A					,       '''output'''			)
																												lowerCAmelCase__			  =      getattr(A					,       '''LayerNorm'''			)
																					elif m_name == "_key_dense":
																												# attention key
																												trace.append('''key'''			)
																												lowerCAmelCase__			  =      getattr(A					,       '''key'''			)
																					elif m_name == "_query_dense":
																												# attention query
																												trace.append('''query'''			)
																												lowerCAmelCase__			  =      getattr(A					,       '''query'''			)
																					elif m_name == "_value_dense":
																												# attention value
																												trace.append('''value'''			)
																												lowerCAmelCase__			  =      getattr(A					,       '''value'''			)
																					elif m_name == "_intermediate_dense":
																												# attention intermediate dense
																												trace.extend(['''intermediate''', '''dense''']			)
																												lowerCAmelCase__			  =      getattr(A					,       '''intermediate'''			)
																												lowerCAmelCase__			  =      getattr(A					,       '''dense'''			)
																					elif m_name == "_output_layer_norm":
																												# output layer norm
																												trace.append('''output'''			)
																												lowerCAmelCase__			  =      getattr(A					,       '''output'''			)
																					# weights & biases
																					elif m_name in ["bias", "beta"]:
																												trace.append('''bias'''			)
																												lowerCAmelCase__			  =      getattr(A					,       '''bias'''			)
																					elif m_name in ["kernel", "gamma"]:
																												trace.append('''weight'''			)
																												lowerCAmelCase__			  =      getattr(A					,       '''weight'''			)
																					else:
																												logger.warning(F"""Ignored {m_name}"""			)
        # for certain layers reshape is necessary
														lowerCAmelCase__			  =      '''.'''.join(A			)
														if re.match(R'''(\S+)\.attention\.self\.(key|value|query)\.(bias|weight)'''					,       A			) or re.match(
														    R'''(\S+)\.attention\.output\.dense\.weight'''					,       A			):
																					lowerCAmelCase__			  =      array.reshape(pointer.data.shape			)
														if "kernel" in full_name:
																					lowerCAmelCase__			  =      array.transpose()
														if pointer.shape == array.shape:
																					lowerCAmelCase__			  =      torch.from_numpy(A			)
														else:
																					raise ValueError(
																					    F"""Shape mismatch in layer {full_name}: Model expects shape {pointer.shape} but layer contains shape:"""
																					    F""" {array.shape}"""			)
														logger.info(F"""Successfully set variable {full_name} to PyTorch layer {trace}"""			)
							return model


def    _snake_case  (	A					,       A					,       A			)		->       Any:
							# Instantiate model
							logger.info(F"""Loading model based on config from {config_path}..."""			)
							lowerCAmelCase__			  =      BertConfig.from_json_file(A			)
							lowerCAmelCase__			  =      BertModel(A			)

							# Load weights from checkpoint
							logger.info(F"""Loading weights from checkpoint {tf_checkpoint_path}..."""			)
							load_tfa_weights_in_bert(A					,       A					,       A			)

							# Save pytorch-model
							logger.info(F"""Saving PyTorch model to {pytorch_dump_path}..."""			)
							torch.save(model.state_dict()					,       A			)


if __name__ == "__main__":
		__UpperCAmelCase       							=				argparse.ArgumentParser()
		parser.add_argument(
		    '''--tf_checkpoint_path''', type=str, required=True, help='''Path to the TensorFlow 2.x checkpoint path.'''
		)
		parser.add_argument(
		    '''--bert_config_file''',
		    type=str,
		    required=True,
		    help='''The config json file corresponding to the BERT model. This specifies the model architecture.''',
		)
		parser.add_argument(
		    '''--pytorch_dump_path''',
		    type=str,
		    required=True,
		    help='''Path to the output PyTorch model (must include filename).''',
		)
		__UpperCAmelCase       							=				parser.parse_args()
		convert_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)

"""simple docstring"""



import math




class 		a_    :







    def   _snake_case       (      self      :							List[Any]  ,       __UpperCamelCase      :							list[list[float]]  ,       __UpperCamelCase      :							list[int]  )		->int:






        '''simple docstring'''






        _UpperCAmelCase        = 0.0
        _UpperCAmelCase        = 0.0
        for i in range(len(__UpperCamelCase  )  ):
            da += math.pow((sample[i] - weights[0][i])  ,       2  )
            da += math.pow((sample[i] - weights[1][i])  ,       2  )
            return 0 if da > da else 1
        return 0







    def   _snake_case       (      self      :							Dict  ,       __UpperCamelCase      :							list[list[int | float]]  ,       __UpperCamelCase      :							list[int]  ,       __UpperCamelCase      :							int  ,       __UpperCamelCase      :							float  )		->list[list[int | float]]:






        '''simple docstring'''






        for i in range(len(__UpperCamelCase  )  ):
            weights[j][i] += alpha * (sample[i] - weights[j][i])
        return weights







def 			_UpperCamelCase						(    )     ->	None:





    """simple docstring"""

    _UpperCAmelCase        = [[1, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 1]]

    # weight initialization ( n, C )
    _UpperCAmelCase        = [[0.2, 0.6, 0.5, 0.9], [0.8, 0.4, 0.7, 0.3]]

    # training
    _UpperCAmelCase        = SelfOrganizingMap()
    _UpperCAmelCase        = 3
    _UpperCAmelCase        = 0.5

    for _ in range(_A   ):
        for j in range(len(_A   )   ):
            # training sample
            _UpperCAmelCase        = training_samples[j]

            # Compute the winning vector
            _UpperCAmelCase        = self_organizing_map.get_winner(_A       ,   _A   )

            # Update the winning vector
            _UpperCAmelCase        = self_organizing_map.update(_A       ,   _A       ,   _A       ,   _A   )

    # classify test sample
    _UpperCAmelCase        = [0, 0, 0, 1]
    _UpperCAmelCase        = self_organizing_map.get_winner(_A       ,   _A   )

    # results
    print(F"""Clusters that the test sample belongs to : {winner}"""   )
    print(F"""Weights that have been trained : {weights}"""   )


# running the main() function
if __name__ == "__main__":
 main()